class_docstr: Defines a generic Scribbles Likelihood based segmentor infertask
class_docstr_tok: ["Defines","a","generic","Scribbles","Likelihood","based","segmentor","infertask"]
code: def pre_transforms(self, data):
      return [
          LoadImaged(keys=["image", "label"]),
          EnsureChannelFirstd(keys=["image", "label"]),
          AddBackgroundScribblesFromROId(
              scribbles="label",
              scribbles_bg_label=self.scribbles_bg_label,
              scribbles_fg_label=self.scribbles_fg_label,
          ),
          Spacingd(
              keys=["image", "label"],
              pixdim=self.pix_dim,
              mode=["bilinear", "nearest"],
          ),
          ScaleIntensityRanged(
              keys="image",
              a_min=self.intensity_range[0],
              a_max=self.intensity_range[1],
              b_min=self.intensity_range[2],
              b_max=self.intensity_range[3],
              clip=self.intensity_range[4],
          ),
      ]
  
file_path: MONAILabel/monailabel/scribbles/infer.py
imports: from monai.transforms import Compose, EnsureChannelFirstd, LoadImaged, ScaleIntensityRanged, Spacingd
  from monailabel.interfaces.tasks.infer_v2 import InferType
  from monailabel.scribbles.transforms import AddBackgroundScribblesFromROId, ApplyGraphCutOptimisationd, MakeISegUnaryd, MakeLikelihoodFromScribblesGMMd, MakeLikelihoodFromScribblesHistogramd
  from monailabel.tasks.infer.basic_infer import BasicInferTask
  from monailabel.transform.post import BoundingBoxd, Restored
label_desc: Use image node_id 2 for calling the ScribblesLikelihoodInferTask obj's underlying member method code with example usage: obj.pre_transforms(data) and returns: list